

依功能與用途,為你挑選 12 款 Chutes 的替代工具。
| 排名 | 工具與標語 | 官方網域 | 每月造訪次數 | 月增幅 | 定價模式 |
|---|
| 主要市場 |
|---|
| 評分 |
|---|
| 比較 |
|---|
| ● | Chutes | chutes.ai | 25.02萬 | -4.49萬 (-15.2%) | 付費 | 美國 | 4.7 | 簡介 |
| 1 | Together AI | together.ai | 164.59萬 | 64.77萬 (+64.9%) | 免費增值、付費 | 美國 | 4.7 | 比較 |
| 2 | DeepInfra | deepinfra.com | 45.78萬 | -4.45萬 (-8.9%) | 付費 | 美國 | 4.6 | 比較 |
| 3 | OpenRouter | openrouter.ai | 1994.14萬 | -132.65萬 (-6.2%) | 付費 | 美國 | 4.8 | 比較 |
| 4 | Novita AI | novita.ai | 27.14萬 | -3.15萬 (-10.4%) | 付費 | 美國 | 4.4 | 比較 |
| 5 | Fireworks | fireworks.ai | 65.97萬 | 3.73萬 (+6%) | 需聯絡報價 | 美國 | 4.6 | 比較 |
| 6 | Replicate AI | replicate.com | 90.4萬 | -8.26萬 (-8.4%) | 付費 | 美國 | 4.6 | 比較 |
| 7 | Groq | groq.com | 398.79萬 | 36.27萬 (+10%) | 付費 | 印度 | 4.7 | 比較 |
| 8 | fal.ai | fal.ai | 309.37萬 | -11.99萬 (-3.7%) | Paid, pay-as-you-go usage-based pricing | 美國 | 4.7 | 比較 |
| 9 | SiliconFlow | siliconflow.com | 27.63萬 | -3.25萬 (-10.5%) | Pay-as-you-go | 美國 | 4.6 | 比較 |
| 10 | Unsloth AI | unsloth.ai | 88.75萬 | -19.01萬 (-17.6%) | 免費 | 美國 | 4.8 | 比較 |
| 11 | LiteLLM | litellm.ai | 57.7萬 | 4.25萬 (+7.9%) | 免費增值、需聯絡報價 | 美國 | 4.8 | 比較 |
| 12 | Ollama | ollama.com | 942.2萬 | 38.97萬 (+4.3%) | Free, Freemium, Paid | 中國 | 4.8 | 比較 |

擅長透過 API 執行與微調各種機器學習模型,但採用了不同的無伺服器架構。

Achieves unmatched inference speed using custom LPU hardware, but supports a more restricted selection of open models.

專項更強Specializes in high-speed serverless inference for generative media models rather than general text LLMs.
Hosts popular open-source models with high performance, though regional availability and developer tooling differ from Chutes.
Hosts popular open-source models with high performance, though regional availability and developer tooling differ from Chutes.
免費 / 開源Focuses on memory-efficient model fine-tuning and local execution rather than a hosted decentralized inference cloud.
免費 / 開源Focuses on memory-efficient model fine-tuning and local execution rather than a hosted decentralized inference cloud.

免費 / 開源Acts as an open-source proxy gateway to unify multiple providers, requiring you to bring your own backend compute.
免費 / 開源Runs open-weight models locally on your own machine instead of providing a cloud-based serverless inference API.
免費 / 開源Runs open-weight models locally on your own machine instead of providing a cloud-based serverless inference API.